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Premortem, a coding-agent-powered airplane blackbox

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Premortem, a coding-agent-powered airplane blackbox

A few weeks ago, I was getting random OOMs on my linux box and I had no idea what was causing them. At one point when I realized that the memory was getting sucked up by some process, I kicked off a claude code job to see if it could figure out what was happening in real time. And it did! In real time the coding agent ran through a suite of system commands, figured out which jobs were causing problems, and then even started to dig into the explicit function calls (python and node processes can both be inspected at the function call level by sideloaded processes) before the entire system finally crashed. Besides being extremely cool, I realized that with a few tweaks I could make this a legitimately useful tool. The basic idea: any time certain system vitals cross a threshold, spin up a coding agent and have the agent debug what is going on as aggressively as possible, with all logs being streamed to a third party server (in addition to being stored on disk). This basic abstraction would solve two huge problems: - Most of the time it is very hard to figure out why exactly a machine went down. This tool would effectively act as an airplane blackbox, a sort of last record of what was going on that specifically is focused on debugging the failure as it happened. Massive speed up on figuring out system-breaking issues. - Most of the time there are available interventions that someone could take that would prevent the system from going down at all, if a human was around when the crash was happening. For example, if I see that I’m about to OOM from vitest, I can just kill a bunch of the processes that are spiking memory and prevent the system from crashing that way. We now have premortem running on all of our production machines. Hope this is useful for other folks!

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.
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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agent, claude · Missing: agents, macos, cursor
63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive
30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
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0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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